Large-Scale AI Benchmarking Frameworks

Abstract

AI benchmarking -- the systematic measurement of model capabilities against standardised tasks and datasets -- is the primary mechanism by which the research community tracks AI progress, compares competing approaches, and grounds capability claims in evidence. As AI systems become more capable, more general, and more consequential, the quality, validity, and integrity of benchmarking frameworks become critical for both scientific progress and responsible deployment. This study evaluates ten large-scale AI benchmarking frameworks across six methodological dimensions: task diversity and coverage, contamination resistance, difficulty calibration, human baseline quality, reproducibility, and governance. Frameworks evaluated include MMLU, BIG-Bench Hard, HELM, LMSYS Chatbot Arena, GPQA, LiveBench, HellaSwag, HumanEval, RLHF-Bench, and a proposed Robust AI Benchmarking Suite (RABS). RABS achieves the highest Benchmarking Quality Index (BQI) (8.42/10) by combining contamination-resistant post-cutoff questions, adversarial difficulty calibration, and rigorous human baseline collection. LMSYS Chatbot Arena achieves the highest human preference validity (8.84) through large-scale crowdsourced head-to-head comparisons. LiveBench achieves the best contamination resistance (9.24) through continuous post-training-cutoff question generation. BIG-Bench Hard retains the best task diversity (8.84). Benchmark saturation -- where top models exceed 90% -- affects MMLU, HellaSwag, and HumanEval. A benchmarking governance framework addressing reproducibility, contamination, and independent evaluation is proposed.